Marcus R. Johnson

Memory interference during language processing.

The authors studied the operation of working memory in language comprehension by examining the reading of complex sentences. Reading time and comprehension accuracy in self-paced reading by college students were studied as a function of type of embedded clause (object-extracted vs. subject-extracted) and the types of noun phrases (NPs) in the stimulus sentences, including relative clauses and clefts. The poorer language comprehension performance typically observed for object-extracted compared with subject-extracted forms was found to depend strongly on the mixture of types of NPs (descriptions, indexical pronouns, and names) in a sentence. Having two NPs of the same type led to a larger performance difference than having two NPs of a different type. The findings support a conception of working memory in which similarity-based interference plays an important role in sentence complexity effects.

Similarity-Based Interference During Language Comprehension: Evidence from Eye Tracking During Reading

The nature of working memory operation during complex sentence comprehension was studied by means of eye-tracking methodology. Readers had difficulty when the syntax of a sentence required them to hold 2 similar noun phrases (NPs) in working memory before syntactically and semantically integrating either of the NPs with a verb. In sentence structures that placed these NPs at the same linear distances from one another but allowed integration with a verb for 1 of the NPs, the comprehension difficulty was not seen. These results are interpreted as indicating that similarity-based interference occurs online during the comprehension of complex sentences and that the degree of memory accessibility conventionally associated with different types of NPs does not have a strong effect on sentence processing.

A cross-cutting approach to enhancing clinical trial site success: The Department of Veterans Affairs' Network of Dedicated Enrollment Sites (NODES) model

Background Recruitment into clinical trials remains a key determinant to study completion and success. While various strategies have been proposed, it is unclear how they apply across different populations, diseases, and/or study goals. The ability to effectively overcome challenges may require different approaches that more broadly focus on addressing obstacles among sites that cannot be overcome by individual studies. Methods The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) established the Network of Dedicated Enrollment Sites (NODES) as a consortium of sites to generate systematic site-level solutions to more efficiently recruit in CSP studies. Initial activities identified priorities and developed approaches through team-based efforts. Metrics were also developed to assess overall network performance. Results Network efforts produced several new strategies and best practices for common problems in CSP research. Recruitment strategies included bringing studies to patients and developing data programs using algorithms for finding eligible patients. Efficiency efforts focused on cross-training and standardizing performance reports. Conclusion NODES addressed site challenges in clinical trial recruitment and management by taking an overall approach that looked at the system rather than individual studies. Practices and operational changes were implemented for CSP research related to recruitment, staff training and research methodology. The network activities suggest that team-based development of tools and insights may help better identify targets and increase efficiencies for clinical trials recruitment.

An assessment of survey measures used across key epidemiologic studies of United States Gulf War I Era veterans.

Over the past two decades, 12 large epidemiologic studies and 2 registries have focused on U.S. veterans of the 1990–1991 Gulf War Era. We conducted a review of these studies’ research tools to identify existing gaps and overlaps of efforts to date, and to advance development of the next generation of Gulf War Era survey tools. Overall, we found that many of the studies used similar instruments. Questions regarding exposures were more similar across studies than other domains, while neurocognitive and psychological tools were the most variable. Many studies focused on self-reported survey results, with a range of validation practices. However, physical exams, biomedical assessments, and specimen storage were not common. This review suggests that while research may be able to pool data from past surveys, future surveys need to consider how their design can yield data comparable with previous surveys. Additionally, data that incorporate recent technologies in specimen and genetic analyses would greatly enhance such survey data. When combined with existing data on deployment-related exposures and post-deployment health conditions, longitudinal follow-up of existing studies within this collaborative framework could represent an important step toward improving the health of veterans.

Research site mentoring: A novel approach to improving study recruitment

Background/Aims The VA Cooperative Studies Programs (CSP) Network of Dedicated Enrollment Sites (NODES) is a consortium of nine VA medical centers (VAMCs) with teams (nodes) dedicated to enhance performance, compliance, and management of CSP multi-site clinical trials. The West Haven CSP Coordinating Center (WH-CSPCC), study coordinating center for CSP #577, Colonoscopy Versus Fecal Immunochemical Test (FIT) in Reducing Mortality from Colorectal Cancer (CONFIRM) trial, and NODES piloted a “site mentoring” (hub-and-spoke) model. In this model, a node site would work one-on-one with a low enrolling CONFIRM site to identify and overcome barriers to recruitment. The aim was to determine the impact of a research site mentoring model on study recruitment and examine site-level characteristics that facilitate or impede it. Results Sites in the mentorship pilot had an average improvement of 5 ± 4 participants randomized per month (min −2.6; max 11.6; SD 4.3). Four of ten sites (40%) demonstrated continuous improvement in the average number of randomized participants per month after the pilot intervention and at three-month follow-up (post-intervention), as compared to the five-month period preceding the intervention. An additional two sites (20%) demonstrated improvement in the average number of randomized participants per month after the pilot intervention, and sustained that level of improvement at three-month follow-up (post-intervention). Additionally, six of ten sites (60%) demonstrated an increased number of participants screened for eligibility immediately following the intervention and at three-month follow-up (post-intervention). Only one site showed a decreased monthly average of randomized participants shortly after the intervention and through the three-month follow-up period. Conclusions The site mentoring model was successful in improving recruitment at low enrolling CONFIRM sites. An additional feasibility assessment is needed to determine if this mentoring model will be effective with other CSP trials.

An initiative using informatics to facilitate clinical research planning and recruitment in the VA health care system

Background Randomized clinical trials are the gold standard for evaluating healthcare interventions and, more generally, add to the medical knowledge related to the treatment, diagnosis and prevention of diseases and conditions. Recent literature continues to identify health informatics methods that can help improve study efficiency throughout the life cycle of a clinical trial. Electronic medical record (EMR) data provides a mechanism to facilitate clinical trial research during the study planning and execution phases, and ultimately, can be utilized to enhance recruitment. The Department of Veterans Affairs (VA) has a strong history of clinical and epidemiological research with over four decades of data collected from Veterans it has served nationwide. The VA Informatics and Computing Infrastructure (VINCI) provides VA research investigators with a nationwide view of high-value VA patient data. Within VA, the Cooperative Studies Program (CSP) Network of Dedicated Enrollment Sites (NODES) is a consortium of nine sites that are part of an embedded clinical research infrastructure intended to provide systematic site-level solutions to issues that arise during the conduct of VA CSP clinical research. This paper describes the collaboration initiated by the Salt Lake City (SLC) node site to bring informatics and clinical trials together to enhance study planning and recruitment within the VA. Methods The SLC VA Medical Center physically houses both VINCI and a node site and the co-location of these two groups prompted a natural collaboration on both a local and national level. One of the functions of the SLC NODES is to enhance recruitment and promote the success of CSP projects. VINCI supports these efforts by providing VA researchers access to potential population pools. VINCI can provide 1) feasibility data during study planning, and 2) active patient lists during recruitment. The process for CSP study teams to utilize these services involves regulatory documentation, development of queries, revisions to the initial data request, and ongoing communications with several key study personnel including the requesting research team, study statisticians, and VINCI data managers. Results The early efforts of SLC NODES and VINCI aimed to provide patient lists exclusively to the SLC CSP study teams for the following purposes: 1) increasing recruitment for trials that were struggling to meet their respective enrollment goals, and 2) decreasing the time required by study coordinators to complete chart review activities. This effort was expanded to include multiple CSP sites and studies. To date, SLC NODES has facilitated the delivery of these VINCI services to nine active CSP studies. Conclusion The ability of clinical trial study teams to successfully plan and execute their respective trials is contingent upon their proficiency in obtaining data that will help them efficiently and effectively recruit and enroll eligible participants. This collaboration demonstrates that the utilization of a model that partners two distinct entities, with similar objectives, was effective in the provision of feasibility and patient lists to clinical trial study teams and facilitation of clinical trial research within a large, integrated healthcare system.